Mechanical wear, overload events, misalignment and structural fatigue can develop in industrial machines progressively, often without any visibility until a failure occurs. To ensure long-term reliability and maintain the highest levels of performance, machines must be continuously monitored under real operating conditions, rather than just during periodic inspections.Critical parameters like force, torque, load, motion, and structural deformation directly determine product quality and throughput. However, in many machines, these forces are still derived, filtered, or compensated post-process, rather than controlled directly at their source. Typical examples include motor current used as a proxy for torque, or hydraulic pressure used to estimate force, both of which introduce model uncertainty and delay.
| Challenge | Solution |
| Fatigue and misalignment develop deep within machinery without early visible signs. | Embedded multi-physical sensing through continuous force, strain and vibration measurement detects anomalies long before failure. |
| Late failure detection disrupts operational continuity and drives up repair costs. | Real-time, high-dynamic, multi-physical measurement captures rapid transient events to warn of impending issues. |
| Relying on indirect indicators makes it difficult to pinpoint the root cause of machine failures. | Direct physical measurement at the source reveals real mechanical causes such as stress, overload, or imbalance, providing clear diagnostic data for engineering decisions. |
| Time-based maintenance intervals or reactive interventions lead to premature servicing, missed faults, and increased downtime. | Continuous monitoring enables condition-based and predictive maintenance strategies, optimising planning and reducing lifecycle cost. |
| Failure data is difficult to structure and exploit, undermining understanding of long-term machine performance. | HBK’s complementary reliability engineering tools structure and analyse failure data for deeper insights, improving decision-making and analysis. |
Capture real mechanical phenomena at the source with a range of high-quality sensors:
Support your understanding of machine health with real-time signal processing and anomaly detection at the edge:
Integrate measurement into your automation and IT systems for unified data flow:
Transform raw data into decisions that optimise machine health, support predictive maintenance, and detect issues earlier.
Machine health monitoring is the continuous measurement and analysis of mechanical behaviour, such as vibration, force, strain and load, under real operating conditions. It enables early detection of degradation, abnormal loads and dynamic instability before failures occur, supporting predictive maintenance and improved machine reliability.
Condition monitoring focuses on long-term machine behaviour, detecting wear, fatigue and degradation over time. In contrast, machine performance control operates in real time to regulate processes and optimise machine output. Health monitoring answers whether the machine is degrading, while control determines if the machine is performing correctly at that exact moment.
AI is used to analyse large volumes of condition data and identify patterns associated with degradation and failure. However, effective AI models depend on high-quality, structured data captured from real machine behaviour. By combining accurate physical measurement with contextualised data, machine health monitoring provides a reliable foundation for AI-driven predictive maintenance.
These faults are identified through changes in vibration signatures, load distribution, strain patterns and dynamic response.
While vibration monitoring detects many dynamic anomalies, it only captures part of the machine's behaviour. A complete strategy requires multi-physical measurement, including force, strain and load data, to identify root causes such as overload or structural stress – factors that vibration alone cannot fully explain.
When combined with structured failure data in systems like FRACAS, machine health monitoring data enables root cause analysis, lifetime and fatigue modelling, the identification of recurring failure modes and the continuous improvement of machine design and maintenance strategies.
This transforms raw measurement data into actionable insights for long-term reliability engineering.
Smart accelerometers integrate vibration sensing, signal conditioning and embedded processing to generate condition indicators such as RMS, spectral features or fault signatures directly at the sensor.
By transmitting these pre-processed metrics instead of raw waveform data, they reduce bandwidth consumption, enable real-time diagnostics, and support scalable, distributed condition monitoring architectures.
This will bring together HBM, Brüel & Kjær, nCode, ReliaSoft, MicroStrain and Discom brands, helping you innovate faster for a cleaner, healthier, and more productive world.
